Scheenstra, A.E.H.
Citation
Scheenstra, A. E. H. (2011, March 24). Automated morphometry of transgenic mouse brains in MR images. Retrieved from https://hdl.handle.net/1887/16649
Version: Corrected Publisher’s Version
License: Licence agreement concerning inclusion of doctoral thesis in the Institutional Repository of the University of Leiden
Downloaded from: https://hdl.handle.net/1887/16649
Prospects for early detection of Alzheimer’s disease from serial MR images in transgenic mouse models
M. Muskulus A.E.H. Scheenstra N. Braakman J. Dijkstra S. Verduyn-Lunel A. Alia
H.J.M. de Groot J.H.C. Reiber
This chapter was adapted from:
Prospects for early detection of Alzheimer’s disease from serial MR images in trans- genic mouse models. Current Alzheimer research. 2009;6(6):503-18.
abstract: The existing literature on the magnetic resonance
imaging of murine models of Alzheimer’s disease is reviewed. Par-
ticular attention is paid to the possibilities for the early detec-
tion of the disease. To this effect, not only are relaxometric and
volumetric approaches discussed, but also mathematical models
for plaque distribution and aggregation. Image analysis plays a
prominent role in this line of research, as stochastic image models
and texture analysis have shown some success in the classification
of subjects affected by Alzheimer’s disease. It is concluded that
relaxometric approaches seem to be a promising candidate for the
task at hand, especially when combined with sophisticated image
analysis, and when data from more than one time-point is avail-
able. There have been few longitudinal studies of mouse models
so far, so this direction of research warrants future efforts.
3.1 Introduction
Alzheimer’s disease (AD) is an age-related neurodegenerative disease characterized by structural brain changes and cognitive dysfunction. Due to the aging in west- ern societies, AD will pose a large psychological and economical burden in the fu- ture [51]. Early detection of AD is therefore of considerable interest, since pharma- cological treatment can reduce the amyloid burden and atrophy of the brain [52, 53].
The atrophy in the brain causes structural changes, which are detectable by various non-invasive imaging modalities [54, 55] and such considerations have led to the de- velopment of new imaging methodologies, for example diffusion-weighted magnetic resonance (MR) imaging [56, 57] multiphoton microscopy [58] or positron emission tomography [59].
The detection of AD by MR imaging techniques [60] is conveniently studied in standardized mouse models [61–67]. Brain mapping techniques [68] can be used to quantify changes, for example in voxel-based morphometry [27,69], and more involved approaches estimate diffeomorphic changes in local brain structure [70] or construct local surface models [31] from volumetric measurements. Texture analysis is an inter- esting alternative [71] that has received little attention so far. The statistical analysis of MR images allows to discriminate between disease conditions [35, 72]. However, these analyses are often static, and do not usually incorporate knowledge about dis- ease dynamics, molecular mechanisms [73,74] or structural changes in time. The latter can in fact be inferred from longitudinal studies [75, 76], whereby animal models are employed favorably [77, 78].
Many extensive review papers have been written on Alzheimer’s disease in the past [65,79–84]; it is not our intention to duplicate previous efforts. However, most reviews on AD concerned with small animal imaging focus on the development of mouse models or different scanning protocols to visualize plaques. In this paper we review the existing work on early detection of AD from serial MR images of transgenic mice, with special regard to the integration of dynamical information, i.e. how does (a) knowledge about AD dynamics from longitudinal studies, (b) knowledge about developmental changes in brain structure and (c) knowledge about disease processes at the molecular level help in the detection process? In particular, statistical and quantitative image analysis methods are addressed, and we subdivide them into volumetric approaches, relaxometric approaches, methods based on plaque burden evaluation, and methods based on texture analysis. Finally we give some recommendations for further research, by indicating gaps in the literature, interesting research directions and problems still to be solved.
3.2 Alzheimer mouse models
Several of the genes involved in the development of familial AD have been isolated in human studies. These genes have been used to develop a wide variety of transgenic mouse models, all displaying one or more of the characteristic pathological features
of the disease [79]. The most common lesions are schematically depicted in figure 3.1:
Senile plaques arising from amyloid-beta (Aβ) accumulation and inflammatory pro- cesses involving glial cells, neurofibrillary tangles (NFT) involving tau protein from the cytoskeleton of affected neurons, and vascular lesions caused by amyloid-beta de- posits in cerebral arteries. The characteristics of several lines of transgenic mouse models [85–106] is given in table 3.1. A more extensive description of these lines is given in Appendix 3.A. Not all available mouse models are described, but those mod- els which have either significantly advanced the understanding of AD pathogenesis or are otherwise in widespread use. This overview is adapted from the work of Mc- Gowan et al. [82], and expanded upon with information obtained from the Alzheimer Research Forum1.
Figure 3.1: Pathological features of Alzheimer’s disease: Normal neuron and synapse (A). Affected neuron in late-stage (B). Normal cerebral artery (C). Affected cerebral vessel (D).
The work of Benveniste et al. [62] showed in 1999 that it is possible to visual- ize plaques in ex vivo samples of human brain by means of MR imaging. In vivo imaging of plaque deposition in human brains has so far not been successfully im- plemented. Visualization of plaques in mouse models at high field strengths has been successful, with first in vivo results reported in 2003 by Wadghiri et al. [107].
Since then, several groups have attempted to visualize plaque burden in vivo in dif- ferent transgenic strains of mice, both with and without the aid of contrast agents.
Furthermore, the development of plaques with age in individual mice has been suc- cessfully tracked using in vivo high resolution magnetic resonance imaging [77]. To date, the most commonly used AD models in this line of MRI research are doubly transgenic APP/PS1 strains [64–67, 78, 107–113], followed by singly transgenic APP strains [67, 77, 78, 107, 114, 115]. PS1 mouse models are occasionally used as controls,
1http://www.alzforum.org/res/com/tra/
in addition to non-transgenic animals, as these animals have elevated Aβ levels, but no Aβ deposits.
3.3 Relaxometry
In addition to anatomical or pathological features, several intrinsic MR parameters can be studied to determine the effect of disease progression. In relaxometric ap- proaches, the T1 (longitudinal, or spin-lattice) and T2 (transverse, or spin-spin) re- laxation rates are commonly studied to facilitate the quantification of disease pro- cesses. T1specifies the rate at which the net magnetization returns to its equilibrium state along the axis of the magnets bore, while T2specifies the rate at which the net magnetization in the transverse plane returns to zero after RF excitation. Alternate relaxation parameters are T2* and T1rho. Unlike T2, the parameter T2* is influenced by magnetic field gradient inhomogeneities and its relaxation time is shorter than the T2 relaxation time. The spin lattice relaxation time constant in the rotating frame, T1rho, determines the decay of the transverse magnetization in the presence of a ”spin-lock” radiofrequency (RF) field [64].
Since both T2 and T1 relaxation times are sensitive to changes in biophysical water content it has been hypothesized that the presence of Aβ deposits in the brain has an effect on these parameters [110]. As such they might be used as independent markers for changes occurring in tissue, averaged over a region of interest (ROI). In fact, even pathological changes below the MRI resolution, i.e. at the subvoxel level, could in principle be detected, as parameter values of a single voxel are the result of an averaging process (partial volume effect). Several groups have studied the effects of the progression of AD on the transverse relaxation rate T2; there is a general consensus that the T2 values of affected brain tissue are lower than in controls, and decrease further as AD progresses [77, 78, 109, 110].
The analysis of relaxometric data in murine models of AD was first reported by Helpern et al. in 2004 [110]. In their work APP/PS1 mice were compared to PS1 mice and non-transgenic littermates. T2 values were found to be significantly lower in the cortex, hippocampus and corpus callosum, when comparing doubly transgenic animals to PS1 and non-transgenic mice, but T1 values did not show significant differences between the three genotypes. Falangola et al. [109] studied APP/PS1, PS1 and non- transgenic mice at two different ages. In addition to reporting a decrease in T2in the APP/PS1 mice, compared to the others, the authors performed image registration to correctly compare specific regions of the brain between the different mice and age groups. In the study by Vanhoutte et al. [115] T2* values were calculated for the cortex and thalamic nuclei in APPV717I mice, which were compared to values in wild type mice. T2* values in the cortex were found to be the same in both groups, but decreased in the ventral thalamic nuclei of transgenics. Braakman et al. studied Tg2576 mice and non-transgenic littermates, starting at 12 months and following them until the age of 18 months [77]. The average T2 values in the cortex and hippocampus of transgenic mice were found to decrease with age. Significant
ModelTransgene(mutation)Promoter PhenotypeReferenceDPAPNFTNDCog PDAPPAPPV717FPDGF++--+[91]
Tg2576APP695(K670N,M671L)PrP++--+[94]
APP23APP751(K670N,M671L)Thy1++-++[88,89,103]
APP717IAPP717IThy1++-++[97]
APPV717FÖADAM10-dnAPPV717IThy1++-++[100]
ADAM10-E384A-HA
TgCRND8APP695,APPV717FPrP++-?+[95]
PS1M146V,PS1M146LPS1M146V,PS1M146LR1EScells-----[90]
PSAPP(Tg2576ÖPS1M146L,PS1M146L,APP695PrP+PDGF++--+[87,93]
PS1-A246E+APPSWE)PS1-A246E,APP695
APPDutchAPPE693QThy1---+?[92]
BRI-Aβ40BRI-Aβ40MoPrP-----[96]
BRI-Aβ42BRI-Aβ42MoPrP++---[96]
JNPL3TauP301LMoPrP--++-[106]
TauP301STauP301SThy1--++-[85]
TauV337MTauV337MPDGF--+++[104]
TauR406WTauR406WMoPrP--++?[105]
rTg4510TauP301LCAMKII--+++[101,102]
HtauHumanPACTau-----[86]
TAPP(Tg2576ÖJNPL3)APP695,TauP301LPrP+PrP+++??[116]
3xTgADAPP695,TauP301L,PS1M146VPrP+PrP++++?[98,99]
Table3.1:ThecharacteristicsoftransgenicmousemodelsofAD.Transgene:PAC,P1artificialchromosome.Promoters:PDGF,platelet-drivengrowthfactor;PrP,prionprotein;MoPrP,mouseprionprotein;CAMKII,calcium/calmodulin-dependentproteinkinaseII.Phenotype;DP,diffuse(pre-amyloid)plaques;AP,amyloidplaques;NFT,neurofibrillarytangles;ND,neurodegeneration;Cog,cognitiveimpairment.Forphenotype:+,positive;-,negative;?,unknown.
decreases of T2 were not observed in controls. Borthakur et al. studied T1rho values in the cortex, hippocampus and thalamus of APP/PS1 mice and controls at ages 6, 12 and 18 months [64]. T1rhovalues decreased in both the transgenic and nontransgenic groups as age increased, however the decrease was significantly more pronounced in the transgenic animals. El Tannir El Tayara et al. studied both T1 [117] and T2[117,118] relaxation rates in APP/PS1 mice, with PS1 animals serving as controls.
They found that T2values in the subiculum of adult APP/PS1 mice were significantly lower than in PS1 mice and could thus serve as an early marker. Young mice (16- 31 weeks) without histochemically detectable iron showed T2 changes, which may indicate that T2 variations can be induced solely by aggregated amyloid deposits.
Falangola et al. studied the changes of T2 in a large group consisting of APP/PS1, APP, PS1 and non-transgenic controls [78]. This study revealed that only the APP and APP/PS1 groups show significant changes in T2 compared to non-transgenic controls. Table 3.2 presents an overview of relaxometric research in AD mouse models.
The statistical analysis of relaxometric data in its simplest form is based on sum- mary statistics over a region of interest (ROI), which is usually much larger than the resolution achieved, encompassing a number of voxels on the order of ten or more. To compare the values of these variables between subjects and over the course of time (in one or more subjects), the images need to be registered with respect to each other.
Between groups of subjects affected by AD and control subjects, there exist significant differences between relaxometric rates. P -values can be derived from the empirical standard deviation by assuming normality of the underlying population and relating this to Student’s t-distribution. Given a large enough population one can even analyze the dependence of the relaxometric data on further parameters, for example gender or behavioral data, by the more general analysis of variance (ANOVA) or general lin- ear models. However, the assumption of normality can be problematic, especially for the limited number of mice usually included in the studies under review [119]; so one better resorts to nonparametric tests such as the Mann-Whitney U test or the compu- tation of Spearman’s rank correlation coefficient. If three or more time points exist, linear regression is usually used, but nonparametric, nonlinear techniques can be more powerful. Permutation tests, in particular, allow the computation of exact p-values for the hypothesis that the summary statistics change in the course of time [120]. To our knowledge, the latter has not yet been applied to the analysis of relaxometric data of AD. Of course, suitable generalizations of ANOVA and linear regression also exist, in the form of generalized linear models (GLM) or mixture models [121, 122].
Ultimately, i.e. for a successful clinical application, the detection of AD should be so robust, and the signal-to-noise ratio so large, that the correct choice of statistical model will be largely irrelevant. At present, however, and especially in the analysis of longitudinal studies, the choice of a correct statistical model is important to increase the sensitivity and to prevent one from drawing the wrong conclusions.
Ref.Relax.
Par. SpeciesN.mice
(Tg/Ntg) NtStat.
anal. ResultsAgeLt.
[110]T1,T2APP/PS1
PS1 9+9/91Student’st-test T2lowerinTgmicethaninNtg.NosignificantchangesinT1de-tected. 16-23m- [109]T2APP/PS1
PS1 9+9/9;
6/6 1?T2decreasedAPP/PS1micecom-paredtocontrols 18m;
6w -
[115]T2*APPV717I4/41N/ADifferencesnotedbetweenTgandNtg 24m- [77]T2Tg25765/54Student’st-test Decreasewithtimedetected12-18m+ [64]T1rhoAPP/PS12/23Student’st-test Significantdecreaseifage>12m6,12,
18m -
[117]T1,T2APP/PS1
PS1 10/9;
13/13 2Pearson,Mann-Whitney,Wilcoxontests NegativecorrelationbetweenT1andageinAPP/PS1animals.T2inthesubiculumofadultAPP/PS1animalswaslowerthaninPS1mice 27-45w;60-86w - [118]T2APP/PS1
PS1 11/101Mann-WhitneyUtest T2isreducedinthesubiculumofAPP/PS1mice;T2isanearlyinvivomarkerofamyloiddeposition 16-31w- [78]T2APP/PS1,
APP,andPS1 64+33+
61/48 3MixedmodelSignificantdecreaseinAPPandAPP/PS1mice 6w-19m+,-
Table3.2:RelaxometrymeasurementsinADmousemodels.Relax.Par,Relaxationparameters;N.mice,numberofmiceincludedinthestudy;Tg,transgenic;Ntg,non-transgenic;Nt,Timepoints;Stat.anal.,statisticalanalysis;Lt.,longitudinalstudy;m,months;w;weeks
3.4 Analysis and models of plaque burden
Ever since Hardy and Higgins stated that the development of Aβ plaques is the main cause of Alzheimer’ disease, leading to neurofibrillary tangles, cell loss, vascular dam- age, and finally resulting in dementia [123]; this theory has been discussed and sup- ported by other findings [124–126]. As mentioned before, the development in plaque burden is still acknowledged as the primary biomarker of Alzheimer’s disease. As Zhang et al. [67] showed, comparing histologically stained plaques with microimaging data (8-24 hrs acquisition time), senile plaques can in principle be reliably identified in ex vivo T2-weighted MR images. However, numerous smaller plaques were not identifiable by visual inspection of the MR images. Later studies have shown that in vivo and ex vivo visualization of both individual plaques and total plaque load can be achieved by MR techniques in reasonable scan times without the aid of contrast agents [64–66,77,110–113,115,127–129]. An overview of the relevant studies of plaque burden in murine brain tissue is shown in Table 3.3.
In general, amyloid plaques are only visible on MRI scans in the later stages of the plaque development. For example, plaque sizes in 12 month old APP/PS1 mice are 19 µm on the average [130, 131], whereas the average voxel size in a MRI slice is around 50Ö50Ö200 µm, which is further discussed in [63, 111, 127]. Therefore, automated, direct detection and analysis of amyloid plaques on MRI scans is useful for analyzing the progression of amyloid deposition, but it cannot be used for early detection algorithms. Of course, indirect detection is still a possibility, since small changes in tissue formation are detectable with the MRI scanner because of the partial volume effect: insufficient image resolution leads to a mixture of the MR parameters of different tissues within a single voxel. In other words, plaques influence the recorded average relaxation rate per voxel proportionally, even in the case that the amount of amyloid deposition is smaller than the sampling volume per single voxel. However, a specific threshold in size for a plaque to be detectable at a prescribed confidence level is not known at present. The analysis of plaque burden by direct imaging could contribute to the latter by supplying the necessary data to set up a more sensitive parametric image model. To this extent, plaque burden analysis has focused on the statistical properties of senile plaques.
In principle, the locations at which plaques appear can be statistically modeled as a spatial point process [132, 133]. However, plaques are spatially extended objects that aggregate, grow and change their shape over the course of time. Stanley and co-workers therefore considered plaques as connected clusters and have found that the cluster sizes in AD human patients follow a log-normal distribution [133]. Moreover, they analyzed the spatial correlation function C(r), i.e. the (normalized) probability of finding another plaque cluster at a distance r from a given cluster [74]. Comparison with randomized surrogate data allowed them to define a characteristic cluster size that changes from about 14 µm at 8 months to 22 µm at 12 months. Moreover, the size of individual plaques has been inferred to be roughly constant in time, with a characteristic length of 1.3 µm, indicating that disease progression consists mainly in accumulation and aggregation of individual plaques.
Following this analysis, Stanley et al. have built a mathematical model for the aggregation and disaggregation of senile plaques on a discrete lattice, i.e. as a random field [73]. This stochastic model also incorporates sudden plaque formation. The latter is consistent with recent evidence that plaques can form rapidly, even within 1-2 days [125]. A more detailed model, incorporating inflammatory processes as well, has been developed by Edelstein-Keshet and co-workers [134].
Shortly thereafter, a chemotactical model emphasizing the role of microglia in the aggregation of senile plaques has been investigated, that unfortunately does not capture the observed dynamics well [135]. The distribution of plaques and microglia, however, seems to be in agreement with observations [136]. For a discussion of mi- croglia in the context of mouse models, see the reviews in [137] and [138]. Imaging of plaques has been addressed in [139], where a mathematical model for the kinetics of PET molecular imaging probes that bind to plaques is proposed.
MR images of senile plaques can be modeled by Markov random field models (or more generally, stochastic image models), where the values of each voxel are considered realizations of a probabilistic process Xij, indexed by coordinates i and j in 2D. For simplicity, these processes are assumed Markovian; to be more precise, the conditional probability P (Xij|Xkl, (i, j) 6= (k, l)) is determined by the distribution of its direct neighbor voxels only:
P (P (Xij|Xkl, (i, j) 6= (k, l)) := P (P (Xij|Xkl, (i, j) 6= (k, l), |i − k| ≤ 1, |j − l| ≤ 1).
Alternatively, such a process is characterized by a Gibbs distribution, i.e. a potential energy associated with each realization (image) [140]. Medical applications of this methodology are mainly found in image segmentation up to now, e.g., of lung tissue or anatomical regions in brain images. In particular, a usable parametric random field model of plaque distributions in brain tissue is still lacking. A different approach to the analysis of plaque distributions in images is the language of fractals, where an image is considered to consist of morphological features that are self-similar, exhibiting the same structural properties at more than one scale. In [141] the authors have found that cortical blood vessel structure, evaluated with fractal-based morphological descriptors, can be correlated with AD pathology. Among other things, estimates of correlation dimension in Alzheimer patients showed smaller values than in controls.
3.5 Cerebral amyloid angiopathy
Alzheimer’s disease is a multi-factorial disease that can be associated with cerebrovas- cular lesions in addition to the aforementioned plaques, the formation of NFT and brain atrophy. In fact, such lesions are often correlated with neurodegeneration. De la Torre and Mussivand suggested in 1993 that a disturbed brain microcirculation can cause Alzheimer’s disease [143] and further studies confirmed that the reduced cerebral blood flow (CBF) that accompanies AD correlates well with the severity of dementia [144, 145]. A possible cause of CBF abnormalities in AD is cerebral amy- loid angiopathy (CAA). This particular form of vascular pathology is caused by the deposition of β-amyloid protein in cerebral vessels [146–148].
Ref.ImagemodalityField Strength StrainN.mice (Tg/Ntg) AgeLong.invivo [107]2D/3DT1SE; 2DT2SE;2DT2*GE
7TAPPandAPP/PS15/5(exvivo) 7/7(invivo)
15-16;5-6m-+,- [67]T2SE9.4TAPP/PS1andAPP2+1/215.5m-- [113]T2FSE7TAPP/PS1,PS12+1/117-19m-- [110]T2FSE7TAPP/PS1,PS19+9/916-23m-- [111]T2SE,T2*GE9.4TAPP/PS1?24-26m-+ [112]T2SE9.4TAPP/PS1?3,6,9,12,24m-+ [115]3DT2*GE7TAPPV717I4/424m-+ [114]3DFSE19F,T1GE9.4TTg2576?16,23m-+ [108]3DT2*GE4.7TAPP/PS1?28,39w-- [77]T2FSE9.4TTg25765/512-18m++ [64]T1rhoGE4.7TAPP/PS12/2,2/2,2/26,12,18m-+ [65]T2SE9.4TAPP/PS1612m++ [142]T2*GE,3DT1 GE,T2SE
4.7TAPP/PS1,PS132/36 (long.7/4)
27-103w++ [128]2D/3DGE, 2D/3DSE,CRAZED
17.6TAPPV717I ÖADAM10-dn
3/516m-+,- [129]3DT2*GE7TAPP/PS1,Tg257620/106-8m;18-20m-+ Table3.3:MRmicro-imagingofsenileplaquesandplaqueburdeninhumansandmousemodels.Imagemodality: 2D/3D,2-or3-dimensional;T1/T1rho/T2/T2*,appliedweightinginMRimagingexperiments;DW,diffusion-weighted; 19F,imagingofFluorine-19labeledcontrastagent;GE,GradientEcho;SE,SpinEcho;FSE,FastSpinEcho;CRAZED, COSYrevampedwithasymmetricz-GEdetection.Long.:indicateswhetherthestudywaslongitudinal
Multiphoton microscopy with a contrast agent showed that plaque development progresses seemingly linearly in Tg2576 mice [149, 150], with an average increase of 0.35% per day in vascular involvement, i.e. vessel area affected. In APPSWE/PS1 mice, CAA progresses slower with a slope of 0.17% per day [151].
Magnetic resonance angiography (MRA) can be applied to visualize vascular struc- tures. The MRA technique differs from MRI in that the signal of stationary tissue is suppressed, and the signal from flowing blood is made visible. MRA is commonly applied to study flow artifacts or defects, to determine whether the vascular structure has been compromised. As in AD neurodegeneration is commonly correlated with CAA, MRA might provide insight into a possibly altered blood supply to specific brain regions. Only a few MRA studies in transgenic mice have so far been reported;
in 2003 Beckmann et al. [152] studied 10 APP23 and 10 control animals at ages 6-7, 11 and 20 months, and observed flow voids in the majority of large brain arteries of APP mice with increasing age, including severe defects such as the absence of one of the carotid arteries. In 2004 Krucker et al. [153] used MRA to non-invasively study the arterial vascular architecture of APP23 mice. Due to the limited spatial resolution of MRA, the in vivo studies were complemented by analysis of the vasculature using vascular corrosion casting. Both techniques revealed age-dependent blood flow alter- ations and cerebrovascular abnormalities in these mice. Thal et al. [154] used MRA to show blood flow alterations in the thalamic vessels of APP23 mice. CAA-related capillary occlusion in the branches of the thalamoperforating arteries of APP23 mice corresponded to the occurrence of blood flow disturbances. Similarly, CAA-related capillary occlusion was observed in the occipital cortex of human AD subjects more frequently than in controls.
3.6 Volumetric methods
Brain atrophy has been pointed out as a biomarker for the development of Alzheimer’s disease in human patients with Mild Cognitive Impairment (MCI) [155–159]. Most studies reported neurodegeneration in the structures of the mesial temporal lobe, such as the hippocampus, parahippocampal gyrus and amygdala, as a result of Alzheimer’s disease. Brain atrophy can be quantified and followed in time by performing volu- metric measurements in MRI. Voxel-based morphometry is an essential step in these types of analysis [160], and it is crucial that the necessary image registration steps are performed correctly [41]. A review paper on this topic was recently published by Ramani et al. [57]. Although there is overwhelming evidence on the utility of volumet- ric biomarkers from human studies, most research in the development of transgenic mouse models has focused on models which develop Aβ aggregation (diffuse plaques and amyloid plaques), usually combined with an emphasis on astro- and microglio- sis. Only in the last decade, small animal research has turned towards the study of neurodegeneration of specific brain regions. Recently it has been found that the progression of amyloid deposition in APP mouse strains is correlated to a decrease in neurogenesis in the hippocampal region [161].
Brain morphometry in transgenic mouse models of Alzheimer’s disease is challeng- ing due to the small size of the structures of interest in the brain and the low contrast between these structures. Although manual segmentation is still considered as the gold standard in morphometric studies, the variability in these findings is large [162].
This problem is overcome by automated segmentation methods, which not only re- duce the amount of time needed for delineation, but also improve the objectivity and repeatability of the segmentation, especially when a brain atlas is used as a tem- plate [63]. MRI enables the creation of digital atlases to describe the anatomy of mice [163, 164] by averaging normalized MRI scans of a group of animals. This has been of considerable interest in the analysis of various phenotypes [10, 162, 165] and is used in the comparative analysis of both in vivo [9] and ex vivo MR images [6, 8].
An example of such an atlas is shown in section 4 in figure 4.2.
To study brain atrophy it is a prerequisite to compare images to an atlas and several studies employ registration algorithms to automatically perform this task.
Nonlinear registration is generally superior to simple affine registration, although it is much more sensitive to noise and image distortion. MR images of high quality are therefore required for nonlinear registration [91]. Another way to study neuroanatom- ical differences between mouse strains, is the statistical analysis of landmark points after nonlinear registration, as employed by Chen et al. [35]. Falangola et al. [109]
applied nonlinear registration techniques to quantify group averages of three distinct mouse strains and proved that nonlinear registration is able to detect small differences of in vivo MR images. In addition to cross-sectional studies, longitudinal studies can be performed. Verma et al. created a longitudinal map of the average brain develop- ment in multiple C57BL/6J mice [166], the so-called spatio-temporal heterogeneity map of brain development and maturation. Recently, Maheswaran et al. applied non linear deformation analysis to both in vivo cross sectional as well as longitudinal studies [167].
3.7 Texture analysis
The texture of an image is an elusive concept that can be roughly defined as its statistical properties at different levels of scale [164]. Above we have already discussed the characterization of plaque burden in terms of stochastic image models, which is a particular approach. Here we discuss three more branches of texture analysis (see also [71, 119, 168, 169]). The statistical approach is specifically targeted at discrimination purposes. To this respect, from a given image or a ROI a number of feature descriptors are computed. The classic example is gray-level co-occurrence matrices (GLCM) in 2D [170]. Let (i,j) be a displacement vector in 2D. For each possible pair (r,s) of gray-level values, i.e. discretized relaxation rates for our purposes, its number of occurrences in the image X is counted. All such co-occurrences define a symmetric matrix: C(r, s | i, j) = |{(k, l)|Xkl = r, Xk±i,l±j = s}|. Two examples are shown in Fig. 3.2, where for simplicity the MR parameter has been discretized at 8 levels. For each of these matrices, distinct statistical measures can be defined. The energy of
the matrix is the sum of its squared entries C2(r, s | i, j), and quantifies the image inhomogeneity. The contrast is the sum of |r − s|2· C(r, s | i, j) over all pairs (r, s), and measures local image variations.
Other commonly used feature descriptors are entropy measures, which can also be directly estimated from images [171]. The latter has been applied to T2 images in a cuprizone mouse model, for example [172]. With respect to Alzheimer’s disease, Freeborough and co-workers used a total of 17 feature descriptors, selected from an initial set of 260 descriptors, most derived from GLCMs, to classify and track the progression of the disease in T1 images of human brains [173]. Liu and colleagues used an initial set of 3456 descriptors to classify T1 images of humans [174]. The cross-validated accuracy exceeded 90 percent in both cases. Kovalev and colleagues discuss the use of discrete anisotropy measures to classify general cerebral pathologies in 3D [175], but do not perform a statistical analysis.
Another branch of texture analysis is the signal processing approach. In its simplest form an image is analyzed in frequency space, i.e. its discrete Fourier transform is the basis for discrimination based on the occurrence of specific frequency components or power changes. A more advanced method is the use of discrete Gabor or wavelet transforms to extract localized frequency information. An example of the latter is the classification of regions in T1-weighted images of human knees with respect to tissue type [176].
More recently, geometric methods have been used to classify MR images. The main idea is to consider the image as consisting of a number of smaller texture el- ements whose distribution indicates changes in structural composition. This was demonstrated on human x-ray mammographic images, classifying them with respect to whether radiological findings were present or not, and this method appears suitable to the analysis of relaxometric data as well [177]. Table 3.4 summarizes the literature on the classification or detection of AD in human studies by texture analysis. Up to now this approach has not been used in mouse models, and only Freeborough et al. [173] consider a longitudinal approach (for the tracking of AD).
3.8 Discussion and conclusion
Summarizing the literature, we can conclude that tracking of relaxometric changes, supplemented by parametric image models and the analysis of image features, is a promising approach to the early detection of the characteristic features of Alzheimer’s disease in mouse models. T2 relaxation times were uniformly found to be the best discriminator, whereas T1 could not sufficiently discriminate between mutants and their controls. Both changes in T2* and T1rho were found to correlate with aging as well, which warrants further research efforts.
Plaque burden analysis of in vivo MRI is comparable to the relaxometric approach.
Figure 3.3 shows the age of mice in weeks for which features of AD are detected with in vivo MRI by several plaque detection methods (light grey bars) and by relaxometric methods (dark grey bars). Since the APP/PS1 mouse model features a more aggres-
Ref. Imaging modality
N.Subjects AD/MCI/C
Time points
Methods Accuracy
[173] T1 MRI 40/0/24
5/0/5
N.A.
2-6
17 statistical 2D feature descriptors
0.91 N.A.
[175] T2 MRI 11
14
N.A.
2
3D Texture anisotropy
N.A.
N.A.
[174] T1 MRI (?) 20/20/20 N.A. 3444 + 12
statistical 2D feature descriptors
> 0.95
[141] Histological
data
? N.A. Morphological
descriptors.
Different results in different areas of the brain
Table 3.4: Texture analysis applied to alzheimer’s disease on human data. AD:
Alzheimers disease, MCI: Mild cognitive complainers, C: controls.
sive progression of pathology development, the results are grouped by APP/PS1 mice and the remaining mice (PS1, Tg2576 and further APP variants).
Direct imaging of plaque burden is very valuable in the creation and validation of mathematical models of plague aggregation. Ultimately, it is desirable to incorporate this knowledge into parametric image models, as this should allow for an increase of sensitivity in the detection of AD from relaxometric images. This is further substan- tiated by the success of texture analysis of MR images. Unfortunately, the few studies undertaken in this regard are phenomenological, and a truly convincing solution for the early detection of Alzheimer’s disease does not yet exist. Related to this is the important problem at which size senile plaques are detectable under a prescribed significance level.
As for the volumetric analysis of MRI data, research in human patients has shown that this approach allows the prediction of the development of AD in patients al- ready suffering from mild cognitive impairment (which does not necessarily lead to AD). However, in small animal research this approach is still in an early phase of development. Obviously, it is difficult to detect and quantify cognitive impairment in animals (confer [178,179] though). The quantification of cerebral amyloid angiopathy by MRA also appears to be promising, especially in tracking the progression of the disease. However, this is again a mostly unexplored area. It has been demonstrated that early detection of AD is feasible by these two approaches, but the results as yet
Figure 3.2: Statistical texture analysis of MR images: Original image (A). Enlarged 10Ö10 subimage (B). Gray level representation with 8 levels (C). Co-occurence ma- trix C(r, s|1, 0) of subimage for horizontal displacement (D). Co-occurence matrix C(r, s|0, 1) for vertical displacement, which describes the statistical properties of the subimage with regard to local variations (E).
are not as convincing as when employing relaxometric data.
In general, MR imaging is very attractive due to its non-invasiveness and its abil- ity to produce images of high quality, and thus very suitable to study Alzheimer’s disease in transgenic mouse models. When performing in vivo imaging on transgenic mice environmental factors, such as the use of anesthetics, stress caused by the imag- ing process, or even the specific mouse strain used are all confounding factors that influence imaging results [180]. Therefore it is necessary to also study the influence of environmental factors in transgenic mouse models of AD, especially in longitudinal or cognitive studies, where the choice of a correct statistical model is important. If sufficient data is available, these effects can be modeled and estimated, for example in a GLM.
Automated analysis of MR images is a nontrivial task. The relatively large differ- ences between scanners, the possibility of artifacts, and the large number of scanning parameters demand standardized imaging protocols and involved methods of image analysis. Automated analysis methods can overcome some of the problems associated with low spatial resolution, low signal-to-noise ratio and inter-group variability, but it seems that there is still a need for the development of new imaging protocols that are specifically targeted at the visualization of amyloid plagues and other symptoms of AD.
With regards to the literature, a striking general observation is that there are rel- atively few longitudinal studies, and almost no effort to utilize temporal information in the detection of AD. On one hand, it is not immediately obvious how to do this.
On the other hand, the main problem in discrimination tasks is the following: there
Figure 3.3: Overview of the minimum age at which Alzheimer pathology was detected in in vivo MRI volumes of APP/PS1 mice (A) and the remaining mouse models (B).
Dark grey denotes a relaxometric method, light grey denotes that AD pathology was determined by plaque burden analysis. None of the studies used contrast agents during imaging.
do exist significant differences between groups of diseased individuals and groups of controls, as the above cited studies have shown. On the level of the individual, how- ever, the (usually considerable) overlap between the two populations renders correct classification difficult. But by following an individual through the course of time, even if it is only a few measurements over the course of a few months, changes can be detected that are otherwise unnoticeable. We believe that this is the key to an early detection of AD, and expect that future studies will be conducted in this direction.
To conclude, small animal imaging will always be ahead of imaging in human patients, as small animals can be exposed to higher field strengths and their aging process is more rapid. This makes murine models the perfect testbed for the devel- opment of detection and screening procedures. Also, small animals provide a way to test and apply new treatment strategies and experimental medication. We should not forget, however, that the ultimate goal of our research efforts is its application to humans.
Acknowledgements The authors wish to thank Janneke Ravensbergen for her contributions to the review of MRA research in transgenic mouse models of CAA and AD.
3.A The most commonly used AD mouse models
just creating some white space PDAPP [91]:
The first mutant amyloid precursor protein (APP) transgenic mouse model with ro- bust plaque pathology. These mice express a human APP cDNA with the Indiana mutation (APPV717F). Plaque pathology starts between 6-9 months in hemizygous PDAPP mice. There is synapse loss, but cell loss and NFT pathology are not ob- served. This model has been used widely in vaccination therapy strategies.
Tg2576 [94]:
This model expresses mutant APPSWEunder control of the hamster prion promoter.
Plaque pathology is observed from approximately 9 months of age onwards. These mice have cognitive deficits but show no cell loss or NFT pathology. Tg2576 is one of the most widely used transgenic models.
APP23 [88, 89, 103]:
These mice express mutant APPSWEunder control of the Thy1 promoter. Prominent cerebrovascular amyloid and cerebral amyloid deposits are observed from 6 months of age onwards. Some hippocampal neuronal loss in this model is associated with amyloid plaque formation.
TgCRND8 [95]:
Mice express multiple APP mutations (Swedish plus Indiana). Cognitive deficits coincide here with rapid senile plaque development at approximately 3 months of age. The cognitive deficits can be reversed by Aβ vaccination therapy.
PS1M146V and PS1M146L [90]:
These models were the first in vivo demonstration that mutant presenilin 1 (PS1) selectively elevates Aβ42 levels. No overt plaque pathology is observed though.
PSAPP (Tg2576ÖPS1M146L, PS1-A246E+APPSWE) [87, 93]:
A bigenic transgenic mouse model which showed that addition of the mutant PS1 transgene markedly accelerates amyloid pathology compared to singly transgenic mu- tant APP mice, demonstrating that the PS1-driven elevation of Aβ42 enhances plaque pathology.
APPDutch [92]:
Mice expressing APP with the Dutch mutation, which causes hereditary cerebral hem- orrhage with amyloidosis in humans, develop severe congophilic amyloid angiopathy.
The addition of a mutant PS1 transgene redistributes the amyloid pathology to the parenchyma, indicating differing roles for Aβ40 and Aβ42 in vascular and parenchy- mal amyloid pathology.
BRI-Aβ40 and BRI-Aβ42 [96]:
These mice express individual Aβ isoforms without over-expression of APP [96]. Only mice expressing Aβ42 develop senile plaques and CAA, whereas BRI-Aβ40 mice do not develop plaques, suggesting that Aβ42 is essential for plaque formation.
JNPL3 [106]:
These mice express 4R0N microtubule associated protein tau (MAPT) with the P301L mutation. This is the first transgenic mouse model with a marked tangle pathology and cell loss, demonstrating that tau protein alone can cause cellular damage and neuronal loss. JNPL3 mice develop motor impairments with age owing to severe pathology and motor neuron loss in the spinal cord.
TauP301S[85]:
This line of mice expresses the shortest isoform of 4R MAPT with the P301S mutation.
Homozygous mice develop severe paraparesis at 5-6 months of age with widespread neurofibrillary pathology in the brain and spinal cord, and neuronal loss in the spinal cord.
TauV337M [104]:
Mice express low level synthesis of 4R MAPT with the V337M mutation (1/10 of endogenous mouse MAPT) driven by the promoter of platelet-derived growth factor (PDGF). The development of neurofibrillary pathology in these mice suggests the nature of tau rather than absolute intracellular tau concentrations drives pathology.
TauR406W [105]:
Mice express 4R human MAPT with the R406W mutation under control of the CAMKII promoter. These mice develop MAPT inclusions in the forebrain from 18 months of age onward and have impaired associative memory.
rTg4510 [101, 102]:
Mice have inducible MAPT using the TET-off system. Abnormal MAPT pathology occurs from one month of age on. These mice show progressive NFT pathology and severe cell loss. Cognitive deficits are evident from 2.5 months of age onwards.
Htau [86]:
These transgenic mice express human genomic MAPT only (mouse MAPT knocked- out). Htau mice accumulate hyperphosphorylated tau from 6 months on and develop Thio-S-positive NFT by the time they are 15 months old.
TAPP (Tg2576ÖJNPL3) [116]:
mice have increased MAPT forebrain pathology when compared to JNPL3 mice, suggesting mutant APP and/or Aβ can affect downstream MAPT pathology.
3ÖTgAD [98, 99]:
This is a triple transgenic model expressing mutant APPSWE and MAPTP301L on a PS1M146V ’knock-in’ background (PS1-KI). This line develops plaques from 6 months on, and MAPT pathology from the time they are 12 months old, strengthening the hypothesis that neurofibrillary pathology can be directly influenced by APP or Aβ
APP717I [97]:
Mice express human APP cDNA with the London mutation (APPV717I). This strain displays decreased exploration, increased neophobicity and increased male aggressive- ness. Pathological features include amyloid plaques and cerebrovascular angiopathy with an onset around 10-12 months, and cholinergic fiber distortion.
APPV717IÖADAM10-dn [100]:
Double transgenic mice expressing both APPV717I and a proteinase of the ADAM (a disintegrin and metalloproteinase) family. Expression of ADAM10-dn leads to an enhancement of the number and size of amyloid plaques in the brains of these double-transgenic mice. However, compared to APPV717Imice, they exhibit improved performance in the Morris water maze test.
